In-wheel motor drive (IWMD) presents a pivotal solution to surmount obstacles in the electrification of transportation technology. However, the increased unsprung mass due to IWMD deteriorates ride comfort. Various control strategies have been proposed to address this issue. Road irregularities limit control systems, with few adapting to changing conditions. Most rely on vision-based monitoring, offering limited road profile data without past or future predictions to improve performance. This study presents an adaptive fractional-order proportional integral derivative (FOPID) controller for a semi-active suspension system of a vehicle using IWMD. The proposed control strategy leverages cloud computing, storing and processing road preview data in a cloud database. Adaptive particle swarm optimization (APSO) optimizes control parameters for enhanced performance, with real-time optimization enabling adaptive acquisition of optimal parameters. Body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL) are used to assess suspension performance. Simulation results confirm the proposed method's effectiveness in improving dynamic performance.

For the green dynamic positioning (DP) of the fully actuated vessel under input magnitude and rate saturations, unknown disturbances, and safe operational region limitations, this paper develops a prescribed-performance Lyapunov-based nonlinear model predictive control (PL-NMPC) scheme with nonlinear thrust allocation (TA). The input magnitude and rate saturations are considered as constraints in the receding horizon optimization (RHO) model. A disturbance observer is designed to provide estimates of the unknown disturbances. A prescribed performance function and an associated error transformation are introduced. Leveraging the disturbance observer and the error transformation, a robust prescribed-performance auxiliary controller is designed to establish a contractive constraint for the RHO model. With the contractive constraint, the developed PL-NMPC scheme inherit the stability of the designed auxiliary controller. Recursive feasibility and the closed-loop stability under the PL-NMPC scheme are provided. Through the theoretical analyses, the fully actuated vessel maintains its position and heading within the safe operational region with minimized energy consumption. Simulation results and comparisons demonstrate the effectiveness and the energy-saving capability of the developed PL-NMPC scheme.

Sports performance is often hindered by unintentional habits, particularly in golf, where achieving a consistent and correct swing is crucial yet challenging due to ingrained swing path habits. This study explores redirection approaches in virtual reality (VR) to correct golfers' swing paths through strategic ball shifting. By initiating a forward ball shift just before impact, we aim to prompt golfers to react and modify their swing motion, thereby eliminating undesirable swing habits. Building on recent research, our VR-based methods incorporate a gradual transformation of visuomotor associations to enhance motor skill learning. In this study, we develop three ball shift patterns, including a novel pattern that employs gradual ball shifts with interspersed normal conditions, designed to retain learning effects post-training. A preliminary study, including expert interviews, assesses the feasibility of various ball-shifting directions. Subsequently, a comprehensive user study measures the learning effects across different ball shift modes. The results indicate that our proposed redirection mode effectively corrects swing paths and yields a sustained learning effect.

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that doctors may compare the patient with typical patients and make decisions from similar cases, we propose a Progressive Prototypical Network (PPN) to select typical patients as prototypes and utilize their information to enhance the representation of the given patient. In particular, a progressive prototype memory and two prototype separation losses are proposed to update prototypes. Besides, a novel integration is introduced for better fusing information from patients and prototypes. Experiments on three real-world datasets demonstrate that our model brings improvement on all metrics. To make our results better understood by physicians, we developed an application at http://ppn.ai-care.top. Our code is released at https://github.com/yzhHoward/PPN.

To improve the handling performance of four-in-wheel-motor-drive (4IWMD) and four-wheel-steering (4WS) vehicles, an integrated control scheme based on active rear steering (ARS) and torque coordination is developed in this study. Considering the tracking of reference states, and the constraints of motor torque and steering angle, the integrated control scheme is designed through model predictive control (MPC). The active rear steering and direct yaw moment generated by the in-wheel motors can assist the vehicle in tracking the desired yaw rate to improve the handling performance. By utilizing the anti-dive forces of the vehicle suspensions, roll motion can be directly controlled via torque coordination without using active suspension. Simulation on a single lane change maneuver is performed, and the results demonstrate that the proposed controller can effectively improve the handling performance and ensure roll stability.

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from life-threatening systemic problems and need to be carefully monitored in ICUs. An intelligent prognosis can help physicians take an early intervention, prevent adverse outcomes, and optimize the medical resource allocation, which is urgently needed, especially in this ongoing global pandemic crisis. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, the rarity of the cases, and privacy concerns. In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data. The transferred parameters are further trained to imitate the teacher model’s representation based on distillation, which embeds the health status more comprehensively on the source dataset. We conduct Length-of-Stay prediction experiments for patients in ICUs on real-world COVID-19 datasets. The experiment results indicate that our proposed model consistently outperforms competitive baseline methods. In order to further verify the scalability of o deal with different clinical tasks on different EMR datasets, we conduct an additional mortality prediction experiment on End-Stage Renal Disease datasets. The extensive experiments demonstrate that an benefit the prognosis for emerging pandemics and other diseases with limited EMR.

Phrase grounding aims to localize the objects described by phrases in a natural language specification. Previous works model the interaction of inputs from text modality and visual modality only in the intra-modal global level and consequently lacks the ability to capture the precise and complete context information. In this paper, we propose a novel Cross-Modal Omni Interaction network (COI Net) composed of a neighboring interaction module, a global interaction module, a cross-modal interaction module and a multilevel alignment module. Our approach formulates the complex spatial and semantic relationship among image regions and phrases through multi-level multi-modal interaction. We capture the local relationship using the interaction among neighboring regions and then collect the global context through the interaction among all regions using a transformer encoder. We further use a co-attention module to apply the interaction between two modalities to gather the cross-modal context for all image regions and phrases. In addition to the omni interaction modeling, we also leverage a straightforward yet effective multilevel alignment regularization to formulate the dependencies among all grounding decisions. We extensively validate the effectiveness of our model. Experiments show that our approach outperforms existing state-of-the-art methods by large margins on two popular datasets in terms of accuracy: 6.15% on Flickr30K Entities (71.36% increased to 77.51%) and 21.25% on ReferItGame (44.91% increased to 66.16%). The code of our implementation is available at https://github.com/yiranyyu/Phrase-Grounding.